Fault diagnosis of multistage centrifugal pump unit using non-local means-based vibration signal denoising

Han, Te; Jiang, Dongxiang

doi:10.17531/ein.2019.4.1

Artykuł - szczegóły

Tytuł artykułu

Fault diagnosis of multistage centrifugal pump unit using non-local means-based vibration signal denoising

Autorzy

Han Te , Jiang Dongxiang

Treść / Zawartość

Pełne teksty:

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Identyfikatory

DOI

10.17531/ein.2019.4.1

Warianty tytułu

Diagnozowanie uszkodzeń wielostopniowej pompy odśrodkowej z wykorzystaniem metody odszumiania sygnału drgań w oparciu o średnie nielokalne

Języki publikacji

Abstrakty

In real industry environment, the signal characteristics of multistage centrifugal pump vibration signal are easily submerged by strong background noise. To settle this problem, the nonlocal means (NLM) approach is proposed for the denoising of multistage centrifugal pump in this paper. Utilizing the similarity theory, the NLM method has achieved a wide range of applications in the fields of image processing and biomedical signal denoising. Due to the periodic characteristics and redundancy, NLM is successfully applied to the de-noising of 1-D machinery vibration signal. The numerical simulation experiments with different SNRs verify the effectiveness and the superiority of the proposed method. Besides, the selection principles of core parameters in NLM are discussed. The real engineering cases analysis demonstrates that the NLM can effectively filter out the background noise and realize the weak fault feature enhancements. The proposed noise reduction method is superior to traditional wavelet coefficient method.

W rzeczywistym środowisku przemysłowym, charakterystyki sygnału drgań wielostopniowej pompy odśrodkowej są zagłuszane przez silny szum tła. Problem ten można rozwiązać stosując zaproponowane w niniejszej pracy podejście oparte na algorytmie średnich nielokalnych (non-local means, NLM). Wykorzystująca teorię podobieństwa metoda NLM znajduje szeroki zakres zastosowań w dziedzinie przetwarzania obrazu i odszumiania sygnałów biomedycznych. Dzięki okresowemu charakterowi i redundancji sygnałów, NLM można z powodzeniem stosować do usuwania szumu jednowymiarowego sygnału drgań maszyn. Skuteczność proponowanej metody i jej przewagę nad stosowanymi dotychczas rozwiązaniami zweryfikowano na podstawie eksperymentów symulacyjnych z uwzględnieniem różnych stosunków sygnału do szumu (SNR). Ponadto omówiono zasady wyboru podstawowych parametrów NLM. Analiza przypadków inżynierskich pokazuje, że NLM pozwala skutecznie odfiltrowywać szumy tła i wzmacniać słabe symptomy akustyczne uszkodzenia. Proponowana metoda redukcji szumów przewyższa tradycyjną metodę współczynnika falkowego.

Słowa kluczowe

vibration signal denoising multistage centrifugal pump nonlocal means diagnosis

odszumianie sygnału drgań wielostopniowa pompa odśrodkowa średnie nielokalne diagnozowanie

Wydawca

Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne

Czasopismo

Eksploatacja i Niezawodność

Rocznik

2019

Tom

Vol. 21, no. 4

Strony

539--545

Opis fizyczny

Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Han Te

hant15@mails.tsinghua.edu.cn

Department of Energy and Power Engineering Tsinghua University Beijing, 100084, China

autor

Jiang Dongxiang

jiangdx@tsinghua.edu.cn

Department of Energy and Power Engineering Tsinghua University Beijing, 100084, China

Bibliografia

1. An X and Yang J. Denoising of hydropower unit vibration signal based on variational mode decomposition and approximate entropy. Transactions of the Institute of Measurement and Control 2016; 38: 282-92, https://doi.org/10.1177/0142331215592064.
2. Buades A, Coll B, Morel J M. A Non-Local Algorithm for Image Denoising[C]// Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on IEEE 2005; 2 :60-65.
3. Darbon J, Cunha A, Chan T F. Fast nonlocal filtering applied to electron cryomicroscopy in IEEE International Symposium on Biomedical Imaging: From Nano To Macro. IEEE 2008:1331-1334, https://doi.org/10.1109/ISBI.2008.4541250.
4. Duan R, Lin Y, Zeng Y. Fault diagnosis for complex systems based on reliability analysis and sensors data considering epistemic uncertainty. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2018; 20(4): 558-566, https://doi.org/10.17531/ein.2018.4.7.
5. Guo-dong Yue, Xiu-shi Cui, Yuan-yuan Zou, Xiao-tian Bai, Yu-Hou Wu, Huai-tao Shi. A Bayesian wavelet packet denoising criterion for mechanical signal with non-Gaussian characteristic. Measurement 2019, 138: 702-712, https://doi.org/10.1016/j. measurement.2019.02.066.
6. Han T, Jiang DX, Zhao Q, Wang L and Yin K. Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery. Transactions of the Institute of Measurement and Control 2018; 40: 2681-2693, https://doi. org/10.1177/0142331217708242.
7. Hernandezsolis A, Carlsson F. Diagnosis of Submersible Centrifugal Pumps: A Motor Current and Power Signature Approaches. Epe Journal European Power Electronics & Drives 2010; 20(1): 58-64, https://doi.org/10.1080/09398368.2010.11463749.
8. Junchao Guo, Dong Zhen, Haiyang Li, Zhanqun Shi, Fengshou Gu, Andrew.D. Ball. Fault feature extraction for rolling element bearing diagnosis based on a multi-stage noise reduction method. Measurement 2019; 139: 226-235, https://doi.org/10.1016/j.measurement.2019.02.072.
9. Kaluer S, Fekete K, Jozsa L, Klai Z. Fault diagnosis and identification in the distribution network using the fuzzy expert system. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2018; 20(4): 621-629, https://doi.org/10.17531/ein.2018.4.13.
10. Kai Zheng, Jiufei Luo, Yi Zhang, Tianliang Li, Jiafu Wen, Hong Xiao. Incipient fault detection of rolling bearing using maximum autocorrelation impulse harmonic to noise deconvolution and parameter optimized fast EEMD. ISA Transactions 2019; 89: 256-271, https:// doi.org/10.1016/j.isatra.2018.12.020.
11. Li, H., Bao, T., Gu, C., Chen, B. Vibration feature extraction based on the improved variational mode decomposition and singular spectrum analysis combination algorithm. Advances in Structural Engineering 2019, 22(7): 1519-1530, https://doi.org/10.1177/1369433218818921.
12. Li Y, Wang X, Liu Z, Liang X, Si S. The entropy algorithm and its variants in the fault diagnosis of rotating machinery: A review. IEEE Access 2018, 6: 66723-66741, https://doi.org/10.1109/ACCESS.2018.2873782.
13. Li Y, Wang X, Si S, Huang S. Entropy based fault classification using the Case Western Reserve University data: A benchmark study. IEEE Transactions on Reliability 2019, 1-14, https://doi.org/10.1109/TR.2019.2896240.
14. Milu Zhang, Tianzhen Wang, Tianhao Tang, Mohamed Benbouzid, Demba Diallo. An imbalance fault detection method based on data normalization and EMD for marine current turbines. ISA Transactions 2017; 68: 302-312, https://doi.org/10.1016/j.isatra.2017.02.011.
15. Saba Adabi, Siavash Ghavami, Mostafa Fatemi, Azra Alizad. Non-Local based denoising framework for in vivo contrast-free ultrasound microvessel imaging. Sensors; 19(2): 245, https://doi.org/10.3390/s19020245.
16. Salman A H , Ahmadi N , Mengko R. Performance Comparison of Denoising Methods for Heart Sound Signal. 2015 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). IEEE, 2015, https://doi.org/10.1109/ISPACS.2015.7432811.
17. Sikora M, Szczyrba K, Wróbel, Michalak M. Monitoring and maintenance of a gantry based on a wireless system for measurement and analysis of the vibration level. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(2): 341-350, https://doi.org/10.17531/ein.2019.2.19.
18. O.I. Traore, L. Pantera, N. Favretto-Cristini, P. Cristini, S. Viguier-Pla, P. Vieu. Structure analysis and denoising using singular spectrum analysis: Application to acoustic emission signals from nuclear safety experiments. Measurement 2017, 104: 78-88, https://doi.org/10.1016/j.measurement.2017.02.019.
19. Ou D, Tang M, Xue R, Yao H. Hybrid fault diagnosis of railway switches based on the segmentation of monitoring curves. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2018; 20(4): 514-522, https://doi.org/10.17531/ein.2018.4.2.
20. Te Han, Dongxiang Jiang, Nanfei Wang. The fault feature extraction of rolling bearing based on EMD and difference spectrum of singular value. Shock and Vibration 2016: 5957179, https://doi.org/10.1155/2016/5957179.
21. Tracey B H, Miller E L. Nonlocal means denoising of ECG signals. IEEE Transactions on Bio-medical Engineering 2012; 59(9): 2383, https://doi.org/10.1109/TBME.2012.2208964.
22. Ville D V D, Kocher M. SURE-Based Non-Local Means. IEEE Signal Processing Letters 2009; 16(11): 973-976, https://doi.org/10.1109/LSP.2009.2027669.
23. Wu Ding-hai, Zhang Pei-lin, Yang Wang-can, Qi Yun-guang. Overlappling group thresholding denoising method based on dual-tree complex wavelet packet transform. Journal of Vibration and Shock 2016; 35(10): 162-166.
24. Yang Shaopu, Zhao Zhihong. Improved Wavelet Denoising Using Neighboring Coefficients and Its Application to Machinery Fault Diagnosis. Journal of Mechanical Engineering 2013; 49(17): 137-141, https://doi.org/10.3901/JME.2013.17.137.
25. Yu G, Yin Y, Wang H, et al. Image denoising based on Non-Local means and multi-scale dyadic wavelet transform in IEEE International Conference on Computer Science and Information Technology. IEEE 2010: 333-336.
26. Yongbo Li, Yuantao Yang, Guoyan Li, Minqiang Xu, Wenhu Huang. A fault diagnosis scheme for planetary gearboxes using modified multiscale symbolic dynamic entropy and mRMR feature selection. Mechanical Systems and Signal Processing 2017, 91: 295-312, https://doi.org/10.1016/j.ymssp.2016.12.040.
27. Zhang Long, Hu Junfeng, Xiong Guoliang. Fault diagnosis of rolling bearings based on weighted nonlocal means algorithm. Journal of Vibration and Shock 2016; 35(19): 156-161, https://doi.org/10.1155/2016/4805383.
28. Zhanxiong Wu, Thomas Potter, Dongnan Wu, Yingchun Zhang. Denoising high angular resolution diffusion imaging data by combining singular value decomposition and non-local means filter. Journal of Neuroscience Methods 2019; 312: 105-113, https://doi.org/10.1016/j.jneumeth.2018.11.020.
29. Zhiwen Liu, Zhengjia He, Wei Guo, Zhangchun Tang. A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery. ISA Transactions 2016; 61: 211-220, https://doi.org/10.1016/j.isatra.2015.12.009.

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